{
 "id": "model-pangu",
 "type": "model",
 "name": "Pangu-Weather (Huawei)",
 "also": [],
 "summary": "A machine-learned global model published with its trained weights for non-commercial use.",
 "url": "https://github.com/198808xc/Pangu-Weather",
 "label": "fact",
 "edges": [
  {
   "verb": "assimilates",
   "to": "data-era5",
   "source": "https://raw.githubusercontent.com/198808xc/Pangu-Weather/main/README.md",
   "read_utc": "2026-11-11T11:28Z",
   "state": "stated",
   "quote": "0.25 degrees. For each 721x1440 slice, the data format is exactly the same as the `.nc` file download from the ERA5 official website. Note that the numpy arrays should be in single precision (`.astype(np.float32)`), not in double precision. We support ERA5 initial fields and ECMWF"
  },
  {
   "verb": "run_by",
   "to": "body-huawei",
   "source": "https://raw.githubusercontent.com/198808xc/Pangu-Weather/main/README.md",
   "read_utc": "2026-11-11T11:28Z",
   "state": "stated",
   "quote": "## Pangu-Weather This is the official repository for the Pangu-Weather papers. [Accurate medium-range global weather forecasting with 3D neural networks](https://www.nature.com/articles/s41586-023-06185-3),"
  }
 ],
 "licences": [
  "lic-pangu"
 ],
 "by": "incident.meteorology@web-summit.dinis",
 "updated": "2026-11-11T11:36Z"
}